Globally, about 3-quarters of births now occur in healthcare facilities, with the proportion being 50% for sub-Saharan Africa, where healthcare-associated infections among newborns are typically 3-20 times higher than in facilities in high-income countries. As this upward trend in institutional deliveries continues, the demand for specialized neonatal care also rises, with dedicated units often only available in tertiary referral hospitals in the case of low- and middle-income countries. Preventing nosocomial infections among vulnerable newborns requires effective and feasible control strategies and interventions. The role of cleaning and cleaners in reducing risks and maintaining a clean safe environment has until very recently been neglected at policy, program, practice, and research levels. There is now an opportunity to reposition cleaning within global and national initiatives related to Water, Sanitation and Hygiene, Infection Prevention and Control, and Antimicrobial Resistance. The evidence base should also be strengthened on cost-effective bundles of cleaning interventions, particularly in the context of low-resource settings. Here increasing overcrowding and shortages of staff and supplies present major threats to neonatal survival and well-being and heighten the case for optimizing the use of low-cost, back-to-basics interventions like cleaning.In the era of translational research, data integration and clinical data warehouses are important enabling technologies for clinical researchers. The OMOP common data model is a wide-spread choice as a target for data integration in medical informatics. It's portability of queries and analyses across different institutions and data are ideal also from the viewpoint of the FAIR principles. Yet, the OMOP CDM lacks a simple and intuitive user interface for untrained users to run simple queries for feasibility analysis. Aim of this study is to provide an algorithm to translate any given i2b2 query to an equivalent query which can then be run on the OMOP CDM database. The provided algorithm is able to convert queries created in the i2b2 webclient to SQL statements which can be executed on a standard OMOP CDM database programmatically.Medical data generated by wearables and smartphones can add value to health care and medical research. This also applies to the ECG data that is created with Apple Watch 4 or later. However, Apple currently does not provide an efficient solution for accessing and sharing ECG raw data in a standardized data format. Our method aims to provide a solution that enables patients to share their Apple Watch's ECG data with any health care institution via an iPhone application. We achieved this by implementing a parser in Swift that converts the Apple Watch's raw ECG data into a FHIR observation. Furthermore, we added the capability of transmitting these observations to a specified server and equipping it with the patient's reference number. The result is a user-friendly iPhone application, enabling patients to share their Apple Watch's ECG data in a widely known health data standard with minimal effort. This allows the personnel involved in the patient's treatment to use data that was previously difficult to access for further analyses and processing. Our solution can facilitate research for new treatment methods, for example, utilizing the Apple Watch for continuous monitoring of heart activity and early detection of heart conditions.State-subsidized programs develop medical data integration centers in Germany. To get infection disease (ID) researchers involved in the process of data sharing, common interests and minimum data requirements were prioritized. In 06/2019 we have initiated the German Infectious Disease Data Exchange (iDEx) project. https://www.selleckchem.com/products/Trichostatin-A.html We have developed and performed an online survey to determine prioritization of requests for data integration and exchange in ID research. The survey was designed with three sub-surveys, including a ranking of 15 data categories and 184 specific data items and a query of available 51 data collecting systems. A total of 84 researchers from 17 fields of ID research participated in the survey (predominant research fields gastrointestinal infections n=11, healthcare-associated and antibiotic-resistant infections n=10, hepatitis n=10). 48% (40/84) of participants had experience as medical doctor. The three top ranked data categories were microbiology and parasitology, experimental data, and medication (53%, 52%, and 47% of maximal points, respectively). The most relevant data items for these categories were bloodstream infections, availability of biomaterial, and medication (88%, 87%, and 94% of maximal points, respectively). The ranking of requests of data integration and exchange is diverse and depends on the chosen measure. However, there is need to promote discipline-related digitalization and data exchange.Electronic documentation of medication data is one of the biggest challenges associated with digital clinical documentation. Despite its importance, it has not been consistently implemented in German university hospitals. In this paper we describe the approach of the German Medical Informatics Initiative (MII) towards the modelling of a medication core dataset using FHIR® profiles and standard-compliant terminologies. The FHIR profiles for Medication and MedicationStatement were adapted to the core dataset of the MIl. The terminologies to be used were selected based on the criteria of the ISO-standard for the Identification of Medicinal Products (IDMP). For a first use case with a minimal medication dataset, the entries in the medication chapter of the German Procedure Classification (OPS codes) were analyzed and mapped to IDMP-compliant medication terminology. OPS data are available at all German hospitals as they are mandatory for reimbursement purposes. Reimbursement-relevant encounter data containing OPS medication procedures were used to create a FHIR representation based on the FHIR profiles MedicationStatement and Medication. This minimal solution includes - besides the details on patient and start-/end-dates - the active ingredients identified by the IDMP-compliant codes and - if specified in the OPS code - the route of administration and the range of the amount of substance administered to the patient, using the appropriate unit of measurement code. With FHIR, the medication data can be represented in the data integration centers of the MII to provide a standardized format for data analysis across the MII sites.